A simple statistical model for prediction of acute coronary syndrome in chest pain patients in the emergency department

Björk, JonasLund University,Lunds universitet,Centrum för ekonomisk demografi,Ekonomihögskolan,Avdelningen för arbets- och miljömedicin,Institutionen för laboratoriemedicin,Medicinska fakulteten,Centre for Economic Demography,Lund University School of Economics and Management, LUSEM,Division of Occupational and Environmental Medicine, Lund University,Department of Laboratory Medicine,Faculty of Medicine (author)

Background Several models for prediction of acute coronary syndrome (ACS) among chest pain patients in the emergency department (ED) have been presented, but many models predict only the likelihood of acute myocardial infarction, or include a large number of variables, which make them less than optimal for implementation at a busy ED. We report here a simple statistical model for ACS prediction that could be used in routine care at a busy ED. Methods Multivariable analysis and logistic regression were used on data from 634 ED visits for chest pain. Only data immediately available at patient presentation were used. To make ACS prediction stable and the model useful for personnel inexperienced in electrocardiogram (ECG) reading, simple ECG data suitable for computerized reading were included. Results Besides ECG, eight variables were found to be important for ACS prediction, and included in the model: age, chest discomfort at presentation, symptom duration and previous hypertension, angina pectoris, AMI, congestive heart failure or PCI/CABG. At an ACS prevalence of 21% and a set sensitivity of 95%, the negative predictive value of the model was 96%. Conclusions The present prediction model, combined with the clinical judgment of ED personnel, could be useful for the early discharge of chest pain patients in populations with a low prevalence of ACS.

Lundager Hansen, JakobLund University,Lunds universitet,Medicin, Lund,Sektion II,Institutionen för kliniska vetenskaper, Lund,Medicinska fakulteten,Medicine, Lund,Section II,Department of Clinical Sciences, Lund,Faculty of Medicine(Swepub:lu)ja8218lu (author)
Ohlsson, MattiasLund University,Lunds universitet,Beräkningsbiologi och biologisk fysik - Genomgår omorganisation,Institutionen för astronomi och teoretisk fysik - Genomgår omorganisation,Naturvetenskapliga fakulteten,Computational Biology and Biological Physics - Undergoing reorganization,Department of Astronomy and Theoretical Physics - Undergoing reorganization,Faculty of Science(Swepub:lu)thep-moh (author)
Edenbrandt, LarsLund University,Lunds universitet,Nuklearmedicin, Malmö,Forskargrupper vid Lunds universitet,Nuclear medicine, Malmö,Lund University Research Groups(Swepub:lu)klfy-led (author)
Öhlin, HansLund University,Lunds universitet,Kardiologi,Sektion II,Institutionen för kliniska vetenskaper, Lund,Medicinska fakulteten,Cardiology,Section II,Department of Clinical Sciences, Lund,Faculty of Medicine(Swepub:lu)kard-hoh (author)
Ekelund, UlfLund University,Lunds universitet,Medicin, Lund,Sektion II,Institutionen för kliniska vetenskaper, Lund,Medicinska fakulteten,Medicine, Lund,Section II,Department of Clinical Sciences, Lund,Faculty of Medicine(Swepub:lu)mphy-uek (author)
Centrum för ekonomisk demografiEkonomihögskolan (creator_code:org_t)

In:BMC Medical Informatics and Decision Making: Springer Science and Business Media LLC6:281472-6947

By the author/editor: Björk, Jonas; Lundager Hansen, ...; Ohlsson, Mattias; Edenbrandt, Lars; Öhlin, Hans; Ekelund, Ulf

About the subject

MEDICAL AND HEALTH SCIENCES: MEDICAL AND HEAL ...; and Health Sciences; and Other Health Sci ...

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.